Four Things about Case Studies: Thing 4: Let Your Data Sell the Story

From Case Study to Success Story – Building Trust in the Water Industry

Introduction

Your Hero saved the day! By picking your solution, your case study
candidate was able to solve their problem and save the ‘world’. The next challenge in writing the success
story is being able to prove your solution worked. Prove it with your case study results. Prove
it in a way that is easy to assess and digest.

By showcasing the case study results
graphically, your success story is that much more believable. In this final
post in the series: Four Things about
Case Studies: From Case Study to
Success Story – Building Trust in the Water Industry we examine the
importance of data visualization in turning a water industry case study into a
brilliant success story.

Thing 4: Let Your Data Sell the Story

In the Results section of a water industry
case study you have the opportunity to prove the benefits of your Solution. Your Hero’s testimonial is even more powerful
when backed by data. Because people
better understand data shown visually, presenting your data in charts and
graphs improves the impact and recall of your story. Data visualization is a powerful tool for
persuading your audience and engendering trust.

Hubspot defines data visualization
as showcasing data, numbers, and
statistics through images and charts. Data visualization is most important
in:

identifying trends;

answering questions;

proving theories;

and, when used in B2B marketing,

showcasing your brand.

Data visualization

Data types

The Oxford English dictionary defines data as: facts and statistics collected together for reference or analysis. But, having large tables full of numbers, no
matter how great, will not help the reader.
You need to present the data in a way the allows analysis. You need a graph!

What graph, you may be wondering? That depends on your data. Understanding what
type of data and data relationships you have allows you to pick the most
effective graph to display the data.

Most data fall into one of two groups:
numerical or categorical:

Numerical
data, also known as quantitative data, have meaning
as a measurement. Numerical data is
either discrete or continuous:

Discrete data can’t be measured but can be counted. Data take on possible values that can be listed out.

Continuous data can’t be counted but can be measured. Their possible values cannot be counted and can only be described using intervals on the real number line.

Categorical data represent characteristics and can be sorted by group or category.

Data relationships

Before you can pick the best visual for a
given data set, you need to understand data relationships. There are seven important data relationships.
The table below defines each type of relationship and gives an example of each.

Charts to visualize data types and relationships

Now to pick the chart. Each different data
and data relationship can be represented by at least one chart type. The trick is to pick the chart that will optimize
analysis. There may be more than one chart
that allows you to visualize the data accurately. In this case, consider what
you’re trying to achieve, the message you’re communicating, and who you’re
trying to reach.

Bar charts

Bar charts are best used to show change
over time, compare different categories, or compare parts of a whole. Bars can be shown vertically, effective for
chronological data, or horizontally, grouped or stacked, effective for
comparing multiple parts-to-whole relationships.

Pie charts

Pie charts are best for making
part-to-whole comparisons, with either discrete or continuous data. They work best with small data sets. Limit your slices to 6 at a maximum.

Line charts

Line charts show time-series relationships
with continuous data. Use line charts to
illustrate trend, acceleration, deceleration, and volatility.

Area charts

Area charts also describe time-series relationships,
but they differ in that they can represent volume as well. A standard area chart is used to show or
compare progression over time. A stacked
area chart visualizes part-to-whole relationships, helping show how each
category contributes to the cumulative total.

Scatter plots

Scatter plots are used to show the
relationship between items based on two sets of variables. They demonstrate correlation in a large
amount of data.

Bubble charts

Bubble charts are excellent for displaying
nominal comparisons or ranking relationships.
The bubble plot is basically a scatter plot with bubbles, good for
displaying an additional variable. A
bubble map is used to visualize values over specific geographic regions.

Chart format

Once you have determined which type of chart
best visualizes your data set, there are some formatting tips that improve the
impact and comprehensibility of your chart.

Tip #1: Label intuitively

Labels help your
reader to interpret the data.
Double-check every chart to make sure the labels are there and correct,
but don’t overdo it. Label data points directly so the reader doesn’t have to
search for the legend. Keep labels on
the x-axis horizontal not tilted.

Tip # 2: Call out or highlight important information

Rather than
relying on a legend alone, use arrows and text, circles or rectangles, or use a
contrasting color to aid interpretation.
Use callouts to highlight relevant information or provide additional
context.

Tip #3: Choose attractive and consistent colors

Choosing the right
color scheme is very important. There
are lots of rules about using color in data visualization. A couple worth noting here include:

Use a single color to represent the same type of data. For instance, if you are depicting a single water quality parameter month by month with a bar chart, use a single color. If you are comparing values between years in a grouped bar chart, use a different color for each year.

Make sure there is enough contrast between colors. If the colors are too similar it can be hard to tell the difference.

Avoid patterns. Patterns can be incredibly distracting. Instead, if, for instance, you are trying to differentiate values on a heatmap, use different saturations of the same color. In the same way, use solid lines rather than dashed lines.

As a rule, don’t use more than 6 colors in a single layout.

Tip # 4: Order the data set

A visualization is
much easier to understand when the data is ordered intuitively. In a bar chart, for example, make sure the
larger values are at the top for horizontal bars, and from left-to-right for
vertical bars.

Order data intuitively. There will be a logical hierarchy in the data. Order categories alphabetically, sequentially, or by value.

Order consistently. The ordering of items in your legend should reflect the order of your chart.

The nature of
these graphs makes them hard to assess.
The tilt required to create the effect skews the reader’s view of the
data.

Tip #6: Choose appropriate data ranges

The range of your
data set is the difference between the highest and the lowest values. In visualizing data, you may need to
consolidate data into groups. When
grouping data, be sure to use consistent ranges. Select three to five numerical ranges that
allow an even distribution of data between them and use +/- signs to extend the
high and low ranges.

Conclusion

Data visualization allows you to showcase
your case study results and prove your solution. By understanding your data and
data relationships you can pick the chart that will let your data sell the
story.

This series has highlighted four things
about case studies that help you tell your success story. By turning a case study into a success story,
you build trust in the water industry:

Are
you so busy making a difference to your clients that you don’t have time to
tell your good news stories? Have you
solved a wastewater problem for a client, a community, a country? Then get that
story out there! Let the world know how
your company solves problems and makes a difference.

That’s where WATER COPY comes in. I research and write top quality science-based ‘good news stories’.